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Abstract

The Asian shore crab, Hemigrapsus sanguineus (De Haan), has recently invaded the Atlantic coast of the United States. Its large populations and continued fecundity are indicative of its successful establishment. Competitive interaction with other coastal crab species has been a subject of many studies. For example, in ex situ experiments, H. sanguineus has been shown to be superior to a long-established invader: the green crab, Carcinus maenas. Because H. sanguineus has been found in such great density on some shorelines, it has been postulated that it poses an ecological threat due to its potential to disrupt native and established species. However, many investigators have noted the invasive crab’s limiting habitat requirement for complex, rocky shorelines. After observation of Point Judith Pond, RI, we assessed whether there were enough rocky areas to support significant populations of H. sanguineus that might be disruptive to C. maenas. Using a photo-quadrat system alongside software analysis, transects were laid across areas of interest. H. sanguineus were collected and counted in each quadrat, allowing habitat metrics obtained from the photographs, such as rock size and cover, to be correlated with the density of crabs, their size, and sex. These data, paired with the observation that Point Judith Pond has ample habitat for C. maenas to be displaced to, led us to conclude that the H. sanguineus population in Point Judith Pond, RI, is likely not capable of causing severe disruption of the native C. maenas population as it currently stands, and that this trend may extend to other estuarine systems.

Author Comment

This updated version contains the figures.

Supplemental Information

H. sanguineus data set

Additional Information

Competing Interests

The authors declare they have no competing interests.

Author Contributions

Nicholas J Jouett conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Funding

The University of Rhode Island's Stan Cobb Marine Biology Endowment (2013 to NJJ) provided the funds to obtain equipment necessary for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1

I would recommend that appropriate regression techniques are used instead of plotting simple linear trends. For example, in figure 2 your dependent variable is a number of observations (counts). These should be predicted using Poisson regression. Also, there seems to be a disproportionally high number of 'zeros'. This would be best modelled using zero-inflated Poisson regression (ZIP models, see this link for example : http://www.ats.ucla.edu/stat/stata/dae/zip.htm).

Using ZIP regression, you would first predict the probability of having 'zero' observations, and then predict counts for non-zero observations. Often, both informations are useful and they are not necessarily related to the same covariates. The GAMLSS of pscl packages in R can help you do this.

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